class_docstr: Implements an ML preprocessing operation.
  
  Preprocessors are stateful objects that can be fitted against a Dataset and used
  to transform both local data batches and distributed data. For example, a
  Normalization preprocessor may calculate the mean and stdev of a field during
  fitting, and uses these attributes to implement its normalization transform.
  
  Preprocessors can also be stateless and transform data without needed to be fitted.
  For example, a preprocessor may simply remove a column, which does not require
  any state to be fitted.
  
  If you are implementing your own Preprocessor sub-class, you should override the
  following:
  
  * ``_fit`` if your preprocessor is stateful. Otherwise, set
    ``_is_fittable=False``.
  * ``_transform_pandas`` and/or ``_transform_numpy`` for best performance,
    implement both. Otherwise, the data will be converted to the match the
    implemented method.
code: def fit_status(self) -> "Preprocessor.FitStatus":
      if not self._is_fittable:
          return Preprocessor.FitStatus.NOT_FITTABLE
      elif (
          hasattr(self, "_fitted") and self._fitted
      ) or self._check_has_fitted_state():
          return Preprocessor.FitStatus.FITTED
      else:
          return Preprocessor.FitStatus.NOT_FITTED
  
imports: import abc
  import base64
  import collections
  import pickle
  import warnings
  from enum import Enum
  from typing import TYPE_CHECKING, Any, Dict, Optional, Union
  from ray.air.util.data_batch_conversion import BatchFormat
  from ray.util.annotations import Deprecated, DeveloperAPI, PublicAPI
label_desc: Use image node_id 2 for calling the Preprocessor obj's underlying member method code with example usage: obj.fit_status() and returns: Preprocessor, Preprocessor, Preprocessor